Metaheuristics in general suffer from the curse of dimensionality [1]} with their performance frequently degrading as the number of decision variables increases [2]}, [3]}, [4]}, [5]}. This is in part due to the increased complexity of the problem, but also due to the exponential growth in the size of the search space [3]}. The training of neural networks presents an ideal context for high-dimensional optimisation as even a medium sized network will have hundreds of weights to optimise.
